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Communication-efficient online detection of network-wide anomalies

Huang Ling, Nguyen XuanLong, Garofalakis Minos, Hellerstein Joseph M., Jordan Michael I., Joseph Anthony, Taft Nina

Απλή Εγγραφή


URIhttp://purl.tuc.gr/dl/dias/55E0F6B6-211B-41CD-A296-44579D0BDAAD-
Αναγνωριστικόhttp://www.cs.berkeley.edu/~jordan/papers/huang-infocom07.pdf-
Αναγνωριστικόhttps://doi.org/10.1109/INFCOM.2007.24-
Γλώσσαen-
Μέγεθος9 pagesen
ΤίτλοςCommunication-efficient online detection of network-wide anomaliesen
ΔημιουργόςHuang Lingen
ΔημιουργόςNguyen XuanLongen
ΔημιουργόςGarofalakis Minosen
ΔημιουργόςΓαροφαλακης Μινωςel
ΔημιουργόςHellerstein Joseph M.en
ΔημιουργόςJordan Michael I.en
ΔημιουργόςJoseph Anthonyen
ΔημιουργόςTaft Ninaen
ΕκδότηςInstitute of Electrical and Electronics Engineersen
ΠερίληψηThere has been growing interest in building large-scale distributed monitoring systems for sensor, enterprise, and ISP networks. Recent work has proposed using principal component analysis (PCA) over global traffic matrix statistics to effectively isolate network-wide anomalies. To allow such a PCA-based anomaly detection scheme to scale, we propose a novel approximation scheme that dramatically reduces the burden on the production network. Our scheme avoids the expensive step of centralizing all the data by performing intelligent filtering at the distributed monitors. This filtering reduces monitoring bandwidth overheads, but can result in the anomaly detector making incorrect decisions based on a perturbed view of the global data set. We employ stochastic matrix perturbation theory to bound such errors. Our algorithm selects the filtering parameters at local monitors such that the errors made by the detector are guaranteed to lie below a user-specified upper bound. Our algorithm thus allows network operators to explicitly balance the tradeoff between detection accuracy and the amount of data communicated over the network. In addition, our approach enables real-time detection because we exploit continuous monitoring at the distributed monitors. Experiments with traffic data from Abilene backbone network demonstrate that our methods yield significant communication benefits while simultaneously achieving high detection accuracy.en
ΤύποςΠλήρης Δημοσίευση σε Συνέδριοel
ΤύποςConference Full Paperen
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2015-11-30-
Ημερομηνία Δημοσίευσης2007-
Θεματική ΚατηγορίαInformation systemsen
Θεματική ΚατηγορίαNetworksen
Βιβλιογραφική ΑναφοράL. Huang, X. Nguyen, M. Garofalakis, J. M. Hellerstein, M. I. Jordan, A. D. Joseph and N. Taft, "Communication-efficient online detection of network-wide anomalies", in 26th IEEE International Conference on Computer Communications, 2007, pp. 134-142. doi: 10.1109/INFCOM.2007.24 en

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